So you've brushed up on ML concepts, practiced Python and SQL for months, and you think you're done with interview prep. But she may be missing the most important and hardest part of the interview to prepare for: problem-solving skills. And based on my experience interviewing other people, as well as conducting interviews myself, I can confidently tell you that this part is, in most cases, what makes or breaks your interviews.
This is the fifth article in the data science interview guide. In previous articles, I have mainly covered technical concepts that often appear in interviews. For reference, the previous articles are listed below:
- Part I. Distribution
- Part II. Probability
- Part III. Basic supervised learning models
- Part IV. Random forest
But unlike most people, I don't think the technical part of the interview is the most difficult; I think soft skills are the hardest to grasp, learn, or teach when it comes to interview preparation. As a manager, that's the most important thing I look for when hiring for my team. Because let's face it, technical skills are easy to brush up on and learn (not just for humans, but for machines too – ChatGPT can implement code pretty well these days), but the ability to understand, solve business problems, and communicate effectively effective it is not. something you can accumulate in a day or two.
This part of the interview is also the most unpredictable as there are fewer templates for the interviewers to follow; On the other hand, many times the interview would go in the direction that you take it. Before you get discouraged, I've outlined a couple of ways soft skills are tested in interviews and tips that can help you prepare for this part of the interview.
Generally, there are two interview modules that fit into the soft skills category: case study (which assesses your ability to carry out structured problem solving) and behavioral interview (which assesses your communication and, again, your ability to structured thinking).
